A new issue of this journal has just been published. To see abstracts of the papers it contains (with links through to the full papers) click here:
Selected papers from the latest issue:Comparative QSARs for antimalarial endochins: Importance of descriptor- thinning and noise reduction prior to feature selection
02 September 2011, 00:22:36
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 1 September 2011
Probir Kumar, Ojha , Kunal, Roy
The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have selected a series of 53 endochin analogues with antimalarial activity against the clinically relevant multidrug resistant malarial strain TM-90-C2B to develop robust QSAR models using different chemometric tools such as stepwise regression, factor analysis followed by multiple linear regression (FA-MLR), factor analysis followed by partial least square (FA-PLS) analysis, genetic function approximation (GFA) and genetic partial least squares (G/PLS) techniques. We have tried to emphasize on importance of descriptor-thinning...
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 1 September 2011
Probir Kumar, Ojha , Kunal, Roy
The emergence of multidrug resistance of the currently available antimalarial drugs has led to the need of the discovery and development of new antimalarial compounds. In the present study, we have selected a series of 53 endochin analogues with antimalarial activity against the clinically relevant multidrug resistant malarial strain TM-90-C2B to develop robust QSAR models using different chemometric tools such as stepwise regression, factor analysis followed by multiple linear regression (FA-MLR), factor analysis followed by partial least square (FA-PLS) analysis, genetic function approximation (GFA) and genetic partial least squares (G/PLS) techniques. We have tried to emphasize on importance of descriptor-thinning...
Quantification and Statistical Significance Analysis of Group Separation in NMR-Based Metabonomics Studies
02 September 2011, 00:22:36
Publication year: 2011
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 1 September 2011
Aaron M., Goodpaster , Michael A., Kennedy
Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures make it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported...
Highlights: ► We demonstrate use of the Mahalonobis distance to quantify cluster separation in PCA scores plots. ► We demonstrate use of an F-test to assess if cluster separations are statistically significant. ► PCA and PLS-DA results are compared, with no scaling and Pareto scaling. ► Widespread use of the techniques will help standardize reporting of metabonomics data. ► Redistribution of significant PCA loadings is demonstrated for Pareto scaling.
Source: Chemometrics and Intelligent Laboratory Systems, In Press, Accepted Manuscript, Available online 1 September 2011
Aaron M., Goodpaster , Michael A., Kennedy
Currently, no standard metrics are used to quantify cluster separation in PCA or PLS-DA scores plots for metabonomics studies or to determine if cluster separation is statistically significant. Lack of such measures make it virtually impossible to compare independent or inter-laboratory studies and can lead to confusion in the metabonomics literature when authors putatively identify metabolites distinguishing classes of samples based on visual and qualitative inspection of scores plots that exhibit marginal separation. While previous papers have addressed quantification of cluster separation in PCA scores plots, none have advocated routine use of a quantitative measure of separation that is supported...
Highlights: ► We demonstrate use of the Mahalonobis distance to quantify cluster separation in PCA scores plots. ► We demonstrate use of an F-test to assess if cluster separations are statistically significant. ► PCA and PLS-DA results are compared, with no scaling and Pareto scaling. ► Widespread use of the techniques will help standardize reporting of metabonomics data. ► Redistribution of significant PCA loadings is demonstrated for Pareto scaling.
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